Estimating repertoire size in a songbird: a comparison of three techniques
Why this work is in the frame
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Bibliographic record
Abstract
Many animals produce multiple types of breeding vocalizations that, together, constitute a vocal repertoire. In some species, the size of an individual’s repertoire is important because it correlates with brain size, territory size or social behaviour. Quantifying repertoire size is challenging because the long recordings needed to sample a repertoire comprehensively are difficult to obtain and analyse. The most basic quantification technique is simple enumeration, where one counts unique vocalization types until no new types are detected. Alternative techniques estimate repertoire size from subsamples, but these techniques are useful only if they are accurate. Using 12 years of acoustic data from a population of rufous-and-white wrens in Costa Rica, we used simple enumeration to measure the repertoire size for 40 males. We then compared these to the estimates generated by three estimation techniques: curve fitting, capture–recapture and a new technique based on the coupon collector’s problem. To understand how sampling effort affects the accuracy and precision of estimates, we applied each technique to six different-sized subsets of data per male. When averaged across subset sizes, the capture–recapture and coupon collector techniques showed the highest accuracy, whereas the curve fitting technique underestimated repertoire size. Precision (the average absolute difference between the estimated and true repertoire size) was significantly better for the capture–recapture technique than the coupon collector and curve fitting techniques. Both accuracy and precision improved as subset size increased. We conclude that capture–recapture is the best technique for estimating the sizes of small repertoires.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it